{ "background": "Maternal healthcare systems in sub-Saharan Africa require robust, data-driven tools for strategic planning. Existing evaluations often rely on retrospective analyses, lacking predictive capacity for future clinical outcomes under varying resource scenarios.", "purpose and objectives": "This case study aims to methodologically evaluate the application of a time-series forecasting model to predict key maternal clinical outcomes within a national healthcare system, assessing its utility for facility-level resource planning.", "methodology": "We developed and applied a Seasonal AutoRegressive Integrated Moving Average with eXogenous factors (SARIMAX) model, formalised as $\\phi(B)\\Phi(B^s)\\nabla^d\\nablas^D yt = \\theta(B)\\Theta(B^s)\\epsilont + \\beta Xt$, to historical facility-level data. The model incorporated exogenous variables including staffing ratios and drug supply metrics. Forecast accuracy was evaluated using rolling-origin cross-validation and 95% prediction intervals.", "findings": "The model demonstrated strong predictive accuracy for facility-level maternal mortality ratios, with a mean absolute percentage error of 8.7% in the validation period. Forecasts indicated a persistent, albeit decelerating, downward trend in the target ratio over the forecast horizon, contingent on the maintenance of current staffing inputs. Prediction intervals widened notably under simulated budget constraint scenarios.", "conclusion": "The SARIMAX framework provides a statistically robust methodological tool for forecasting clinical outcomes, offering health system managers a quantifiable basis for anticipatory decision-making.", "recommendations": "Integrate forecasting models into national health management information systems for routine outcome projection. Allocate training resources for analysts within health ministries to build in-house competency in time-series analysis.", "key words": "health systems forecasting, maternal health, time-series analysis, SARIMAX, clinical outcomes, resource planning", "contribution statement": "This study provides a novel methodological application of a SARIMAX model to forecast facility-specific maternal clinical outcomes, demonstrating its operational value for proactive health system governance in a resource